Seven Categories of Intangible Assets That Productivity Statistics Ignore

When Carol Corrado, Charles Hulten, and Daniel Sichel published their landmark paper in 2005 identifying three broad categories of intangible capital — computerised information, innovative property, and economic competencies — they drew a map of an economy that statistical agencies were only beginning to recognise. Two decades later, that map has been only partially incorporated into national accounts.

The 2008 System of National Accounts brought R&D and software within the capital boundary. The 2025 SNA revision will add data assets and provide clearer definitions around AI and cloud services. But even after these revisions, the majority of what businesses actually invest in to build competitive advantage — and the majority of what drives their productivity — remains outside the measurement framework.

Here are the seven categories where the blind spot is most consequential.

1. Organisational Capital

This is arguably the largest and least measured category of intangible asset. Organisational capital encompasses management practices, internal processes, decision-making frameworks, governance structures, and the accumulated institutional knowledge that determines how effectively a firm converts inputs into outputs.

The World Management Survey, which has assessed management quality across thousands of firms in dozens of countries, consistently finds that differences in management practices explain a substantial share of cross-firm and cross-country productivity variation. Firms in the top quartile of management quality are significantly more productive than those in the bottom quartile, even within the same industry and country.

Yet organisational capital appears nowhere in national accounts. When a company invests in redesigning its operating model, implementing new management systems, or restructuring its decision-making processes, that investment is treated as a current expense. It generates no asset on any balance sheet, public or national.

2. Proprietary Data and Analytics Capabilities

Data has been called "the new oil," but unlike oil, it does not appear as a natural resource or a produced asset in most statistical frameworks. Companies that invest millions in building proprietary datasets, developing analytics pipelines, and training machine learning models on their data are creating durable productive assets — assets that generate returns over many years.

The 2025 SNA revision acknowledges this by creating a new category for "data and databases." This is progress. But the practical challenges of measuring data assets are immense. How do you value a proprietary customer behaviour dataset? How do you account for the fact that the same data can be used simultaneously across multiple business functions without being depleted? How do you handle data that increases in value as it grows, exhibiting increasing rather than diminishing returns?

Until these questions are resolved in practice — not just in theory — productivity statistics will systematically undercount the capital inputs of data-intensive firms and sectors.

3. Human Capital and Workforce Training

The skills, expertise, and tacit knowledge embedded in a workforce are among the most important productive assets any firm possesses. When a company invests in training programmes, professional development, mentoring systems, and knowledge-sharing infrastructure, it is building an asset that directly increases labour productivity.

National accounts treat all of this as current expenditure. The ONS and other agencies produce satellite accounts for human capital, but these are experimental and are not integrated into the core productivity statistics that policymakers and investors rely on. The result is a systematic understatement of capital formation and, consequently, an overstatement of total factor productivity in periods when training investment is stable, and an understatement when it is growing.

For the UK, this matters enormously. The Productivity Institute has identified underinvestment in skills and training as one of the key drivers of the UK's productivity gap relative to peer economies. But if we do not measure the asset being built by that investment, we cannot accurately quantify the gap.

4. Brand Equity and Reputation

Strong brands reduce customer acquisition costs, enable premium pricing, create barriers to entry, and provide resilience during economic downturns. They are, by any reasonable definition, productive assets.

Accounting standards recognise acquired brands (those purchased as part of a business combination) as intangible assets on the balance sheet. But internally developed brands — which represent the vast majority of brand value in the economy — are expensed as incurred. The marketing, advertising, public relations, and content creation expenditure that builds brand equity over years or decades is treated as though it is consumed in the period it occurs.

For productivity measurement, this creates a double distortion. The investment that builds the brand is not counted as capital formation. And the output enabled by the brand — the pricing power, the customer loyalty, the reduced cost of sales — is captured in revenue figures without being attributed to the asset that generated it.

5. Customer Relationships and Network Effects

A firm's installed customer base, its contractual relationships, its position within supply chain networks, and the network effects generated by its user community are all sources of durable productive value. SaaS companies, for example, invest heavily in customer acquisition and onboarding, creating assets — recurring revenue streams, usage data, upsell opportunities — that generate returns over years.

None of this investment is capitalised in financial statements. None of it appears in national accounts. The entire customer-related intangible asset category is invisible to productivity measurement.

This matters particularly for understanding productivity in services sectors, which now dominate advanced economies. The relationship between input and output in services is mediated by customer relationships in ways that have no parallel in goods production. Ignoring these assets means ignoring a fundamental driver of services productivity.

6. Intellectual Property Beyond R&D

National accounts have made significant progress in capitalising R&D expenditure, and the treatment of software and databases is improving. But the broader intellectual property landscape — design capabilities, proprietary methodologies, trade secrets, process innovations that do not qualify as formal R&D — remains largely unmeasured.

Consider a consulting firm that develops a proprietary diagnostic framework, or a logistics company that creates a novel routing algorithm through operational experimentation rather than formal research. These are genuine intellectual property assets that generate returns over time. But because they were not produced through activities classified as R&D, they fall outside the measurement boundary.

The distinction between what counts as R&D and what counts as routine business activity is increasingly arbitrary in a knowledge economy. Many of the most productively significant innovations emerge from operational learning and iterative improvement rather than from dedicated research programmes.

7. Ecosystem Position and Strategic Relationships

In platform economies and complex value chains, a firm's position within broader ecosystems — its partnerships, alliances, integration agreements, and platform relationships — constitutes a productive asset. A firm that occupies a strategically advantageous position in a value network can capture disproportionate value relative to its measured inputs.

This category is perhaps the hardest to measure, but its omission creates real distortions. When productivity statistics show that certain firms or sectors are extraordinarily productive, part of that apparent productivity may actually reflect unmeasured ecosystem capital rather than genuine efficiency in converting inputs to outputs.

The Cumulative Impact

Each of these categories individually represents a significant measurement gap. Taken together, they suggest that our current productivity statistics are systematically blind to the majority of the capital that drives economic performance in advanced economies.

Research consistently estimates that intangible capital, broadly measured, accounts for 60-70% of firm value. If even half of this intangible capital is poorly captured or entirely absent from productivity statistics, then our understanding of productivity growth, productivity levels, and the drivers of productivity differences across firms, sectors, and countries is fundamentally incomplete.

This is not a call for measurement perfection — that is unattainable. It is a call for measurement ambition commensurate with the scale of the problem. Statistical agencies, accounting standard-setters, and the research community need to treat intangible asset measurement not as a secondary research programme but as the central challenge of economic measurement in the 21st century.

At Opagio, we work at the firm level to make these invisible assets visible — identifying, categorising, and valuing the intangible capital that drives business performance. The aggregate statistics will only improve when we have better micro-level data to build from.

David Stroll is CTO of Opagio, which specialises in the identification and valuation of intangible business assets. He brings 40 years of experience in strategy, technical systems delivery, and macro-economic theory (FTSE 250).